[Expression of Interest] Data Engineer

Role Summary

We are looking for a talented and driven engineer to join the Ascend Sandbox team — Experian's cloud-based analytical platform that empowers clients and data scientists to explore, model, and derive insights from data in a secure, governed environment. You will play a key role in building, maintaining, and scaling the platform that serves both internal teams and external clients across the APAC region.

What you’ll be doing

• Design, build, test, deploy and operate scalable batch and, where applicable, streaming data pipelines for the Ascend Sandbox analytical platform.
• Develop high-quality Python, PySpark and SQL solutions to ingest, transform and process large-scale datasets with accuracy, reliability and efficiency.
• Design data models and reusable, trusted datasets/data products for analytics, data science and downstream applications.
• Engineer and optimise AWS-based data solutions using relevant services such as Amazon S3, AWS Glue, Amazon Athena, Amazon Redshift and Amazon EMR, according to the platform architecture.
• Apply data lake and lakehouse engineering practices, including efficient storage formats, partitioning, schema evolution and storage/query optimisation.
• Design robust ETL/ELT patterns, including incremental processing, dependency management, idempotency, retries, backfills and failure recovery.
• Implement workflow orchestration and scheduling for data pipelines using the platform's approved tooling, such as Airflow/MWAA, AWS Step Functions or AWS Glue Workflows.
• Build automated data quality controls and monitoring for completeness, validity, reconciliation, schema compliance, freshness, pipeline health and processing SLAs/SLOs.
• Optimise Spark workloads and distributed data processing for performance, scalability and cost, including partitioning, joins, shuffles, skew and resource utilisation.
• Implement practical data governance, metadata, lineage, cataloguing, security and access-control requirements across data pipelines and datasets.
• Collaborate with clients, business users, data scientists and technical teams to translate business and data requirements into scalable data-processing solutions and integrations.
• Investigate and resolve production data and pipeline issues, perform root-cause analysis, and implement preventive improvements to reliability and operational efficiency.
• Ensure data engineering solutions comply with established architecture, coding, testing, security and quality standards, and contribute to code reviews, CI/CD and automated deployment practices.

• Must possess a Degree / Advanced Diploma in Computer Science, Data Engineering, Software Engineering or equivalent. Seniority will be commensurate with relevant experience and qualifications.
• At least 6 years of experience in data engineering or developing data-intensive systems, including the design, build and production support of data pipelines.
• Strong hands-on experience with Python and PySpark for large-scale and distributed data processing.
• Strong SQL skills, including complex transformations, joins, window functions, query optimisation and performance tuning.
• Hands-on experience designing and operating scalable ETL/ELT data pipelines, including incremental loads, retries, backfills, dependency management and failure recovery.
• Experience with data modelling for analytical use cases, including dimensional modelling, facts/dimensions, normalization/denormalization and handling historical data.
• Hands-on experience with AWS data services relevant to data engineering, such as Amazon S3, AWS Glue, Amazon Athena, Amazon Redshift and Amazon EMR; exposure to Lambda, Kinesis/MSK, DMS or Lake Formation is an advantage where relevant.
• Experience with data lake/lakehouse concepts, storage layout, partitioning, schema evolution and efficient data formats such as Parquet; familiarity with JSON, CSV and Avro is useful.
• Strong understanding of Spark performance engineering, including partitioning, shuffles, joins, data skew, parallelism, memory/resource optimisation and troubleshooting.
• Experience with pipeline orchestration and workflow scheduling using tools such as Airflow/MWAA, AWS Step Functions, AWS Glue Workflows or equivalent.
• Experience implementing automated data quality checks and data observability, including monitoring pipeline health, data freshness, volumes, failures and quality anomalies.
• Practical knowledge of data governance, metadata management, data cataloguing, lineage and schema management.
• Practical understanding of AWS data security, including IAM/least privilege, encryption/KMS, secrets management, S3 security and data-access controls.
• Familiarity with Infrastructure as Code such as Terraform, AWS CloudFormation or AWS CDK is an advantage.
• Understanding of cloud cost and performance optimisation for storage, compute, Spark workloads and query patterns.
• Experience with Git-based workflows, branching strategies, code reviews, CI/CD pipelines and automated deployment. Familiarity with Jira, Bitbucket, Jenkins and Checkmarx is an advantage.
• Experience writing automated unit, integration and data-pipeline tests for Python/PySpark transformations and data quality.
• Strong testing, debugging, troubleshooting and root-cause analysis skills in production data environments.
• Good understanding of Agile, SCRUM, SDLC and software engineering practices.
• Strong communication and stakeholder management skills with the ability to engage clients, business users, data scientists and technical teams.
• Strong analytical thinking, problem-solving, teamwork and willingness to learn, with the ability to work independently when required and deliver under tight deadlines.
• Familiarity with AI tools for coding assistance, documentation or automation is a plus.

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Experian Careers - Creating a better tomorrow together

Find out what its like to work for Experian by clicking here